Tagline
AI is changing science. Learn about the frontier.
Vision
Science is a living process. AI allows us to cultivate new ways of thinking, growing beyond traditional boundaries.
Number of sessions
4 sessions total (2 hours each)
Who it's for
Students and researchers
Recommended for
Those who want to incorporate AI into their own research
Those who want to learn about the latest AI technology and its scientific applications
Those who want to apply the AI for Science trend to their own research
Prerequisites
No programming experience required.
A basic background in science is sufficient.
Course features
Systematic learning
From the history of AI to Agentic Science
Learn the big picture systematically in 4 sessions
Hands-on format centered on demos
Through demos using real tools
Gain knowledge you can use starting tomorrow
Covers the latest case studies
AlphaFold, GraphCast, MatterGen
Introducing the world's leading cutting-edge research
Hands-on practice with no code
No programming required.
You can put it to use in your research right away
Course strengths
1.
The latest generative AI technology
Systematically master the principles and applications of cutting-edge technologies that accelerate scientific discovery, such as LLMs and diffusion models
2.
Hands-on format centered on demos
Through demos using real tools
Gain knowledge you can use starting tomorrow
3.
No-code utilization
Removes the programming barrier so that researchers with specialized knowledge can learn to use AI tools intuitively.
4.
Insights from Matsuo Lab
From Matsuo Lab,
Japan's foremost AI research hub —
providing the latest, deepest insights available.
Curriculum
Session 1
Introduction to AI for Science
History of AI and scientific research, the ecosystem, and the big picture of AI-driven research
- The history of AI and the paradigm shift in scientific research
- The fifth paradigm: “AI-driven science”/ Scientific ML
- Key players (DeepMind, Microsoft, NVIDIA, RIKEN, TRIP)
Session 2
Applications in physics, science, and materials science
How LLMs work, PINNs, GNNs, and the latest examples in weather forecasting and materials discovery
- Fundamentals of LLMs (large language models), PINNs, and GNNs
- Case study: GraphCast (1,000x faster weather forecasting)
- Case study: GNoME-MatterGen (new materials discovery and inverse design)
Session 3
Life sciences, drug discovery, and global trends
AlphaFold, Agentic Science, and national AI strategies
- AlphaFold3: 2024 Nobel Prize in Chemistry
- AI drug discovery pipeline / Self-Driving Labs / Agentic Science
- Global AI for Science strategies (United States, China, EU, Japan)
Session 4
Hands-on: Accelerating the research process
Deep Research, reading academic papers, and hands-on practice with no-code tools
- Paper search tools (Semantic Scholar, Elicit, Perplexity)
- Demo: Using Deep Research, ChatGPT, Gemini, and Claude
- Demo: Building a paper Q&A system with Dify
Related URL
https://www.elith.ai/ai-education-course/ai-for-science
